Patent-grade invention disclosures for things that should exist but don't. Published to the public domain so nobody can lock them up.
Every technical claim is specific enough to constitute prior art under 35 U.S.C. Β§ 102. Every reference is real. New disclosure every day. Verified timestamps on GitHub β
A system exploiting the passive bistatic radar capability inherent in consumer WiFi mesh networks to detect and classify unauthorized drones. Standard 802.11ax/be access points transmit null data packets at 500-1,000 Hz, while neighboring APs extract per-subcarrier CSI and apply STFT analysis to identify rotor blade micro-Doppler signatures at blade-pass frequencies of 150-400 Hz. A lightweight CNN discriminates quadcopters from birds, pedestrians, and vehicles using features including blade-pass frequency, harmonic ratio vectors, and rotor RPM asymmetry patterns unique to multi-rotor UAVs. Multi-AP fusion estimates 3D trajectory via bistatic ellipsoid intersection and Doppler triangulation.
A system exploiting the multi-microphone arrays on consumer smart glasses to perform real-time noise source classification into 14 urban sound event categories, direction-of-arrival estimation via GCC-PHAT across the 140 mm ear-to-ear baseline, and crowdsourced fusion of geotagged observations into city-scale noise maps at 10-meter spatial resolution. Only 36-byte aggregated metrics leave the device, preserving privacy while enabling source triangulation, municipal enforcement reporting, personal noise dose tracking, and quiet-route pedestrian navigation.
A system repurposing the existing speaker and multi-microphone array hardware on consumer smart glasses to perform biomimetic acoustic echolocation for infrastructure-free indoor spatial mapping. Near-inaudible coded chirps (17β20 kHz) emitted through temple speakers are captured by the 5β6 microphone array, processed by an on-device neural network to reconstruct room geometry and detect obstacles, and fused with visual SLAM for robust navigation in visually degraded conditions including darkness, smoke, and featureless corridors.
A system using crowdsourced wearable physiological data β skin temperature, PPG-derived peripheral vasomotor tone, and electrodermal activity β to generate real-time street-level thermal comfort maps. On-device inference computes a Physiological Thermal Comfort Score without transmitting raw biometrics, differential privacy protects individual data, and a graph convolutional network fuses sparse crowd reports with environmental covariates for continuous 25-meter resolution comfort maps.
A system that repurposes residential solar panel smart inverter I-V curve data to perform angular-dependent transmittance spectroscopy on panel soiling deposits, separating organic from mineral components to infer nearby vegetation fuel moisture and dryness. A graph neural network aggregates soiling composition signals from thousands of installations to produce neighborhood-scale wildfire ignition risk maps and predictive alerts days before critical fire weather events.
A system using multi-antenna arrays on consumer wearable devices (smart glasses, earbuds, watches) to passively detect, classify, and spatially locate covert electronic surveillance devices by their unintentional electromagnetic emissions. On-device neural classification distinguishes hidden cameras, audio recorders, GPS trackers, and IMSI catchers from legitimate electronics, with results rendered as AR spatial markers.
A system using wearable IMU gait perturbation signatures as a low-power attention trigger for camera-based visual classification of pedestrian accessibility barriers (broken sidewalks, missing curb cuts, excessive cross-slopes, obstructions), enabling passive crowd-sourced mapping of ADA compliance gaps at city scale with 85-92% power savings over continuous video analysis.
Disclosed is a system and method for continuously estimating indoor relative humidity using the built-in microphone and speaker hardware present in consumer smart speakers. The system exploits the well-characterized physical phenomenon of frequency-dependent atmospheric sound absorption caused by molecular relaxation processes in oxygen and ...
A system that repurposes existing consumer IoT microphones in smart speakers, video doorbells, and security cameras as a distributed acoustic detection network for unauthorized drone intrusion. Each device runs an on-device CNN classifier for multi-rotor harmonic signatures, and a cooperative TDOA protocol geolocates drones across heterogeneous devices without transmitting raw audio.
Disclosed is a system and method for real-time empathy calibration across artificial intelligence interaction modalities β text-based chat, voice-based conversation, and embodied smart-display interfaces β using multimodal affect recognition and adaptive response generation. The system continuously measures user emotional state via three signal ...
Disclosed is a system and method for dynamically decomposing expert service requests into AI-automatable and human-required subtasks within a marketplace platform. The system employs a task decomposition engine that analyzes incoming service requests using a fine-tuned large language model to identify discrete work units, classify each unit's au...
Disclosed is a system and method for coordinating residential battery storage arrays as a virtual power plant (VPP) that performs real-time energy arbitrage and ancillary grid services. The system predicts individual household load profiles using a recurrent neural network trained on smart meter data, forecasts wholesale electricity prices using...
Disclosed is a system and method for dynamically calibrating the difficulty of AI-generated tutoring interactions to maximize long-term knowledge retention using principles from cognitive science. Unlike conventional AI tutoring systems that optimize for session-level performance (maximizing correct answers and engagement time), the disclosed sy...
Disclosed is a system and method for continuous federated fine-tuning of enterprise large language models (LLMs) that captures organizational context from heterogeneous internal data sources β including email archives, document repositories, Slack workspaces, and meeting transcripts β without centralizing sensitive data on any single server or t...
Disclosed is a system and method for training robotic manipulation policies on household objects using a procedurally generated simulation environment that models realistic physical properties of common kitchen and domestic items. The system employs domain randomization across object geometry, surface friction, mass distribution, deformability, ...
Disclosed is an open standard and reference implementation for encoding, exporting, and importing personal AI context β including conversation history, learned preferences, behavioral patterns, and knowledge graphs β between AI assistants from different providers. The standard defines a portable AI Memory File format (.aim) with versioned schema...
Disclosed is a system for continuous health risk assessment that fuses data from consumer wearable sensors (photoplethysmography, accelerometer, skin temperature, SpO2), at-home diagnostic devices (blood pressure cuffs, continuous glucose monitors, smart scales with bioimpedance), and patient-reported outcomes into unified risk scores for acute ...
Disclosed is a protocol and runtime system for orchestrating AI inference across heterogeneous edge devices β smartphones, smart speakers, AR glasses, laptops, smart home hubs, and IoT devices β connected within a local network. The system dynamically partitions neural network computation across available Neural Processing Units (NPUs) based on ...
Disclosed is a measurement and recommendation system for digital community platforms that optimizes for participant meaning and purpose rather than engagement. The system instruments user interactions with adapted versions of validated psychological scales β the Purpose in Life Test (PIL), Meaning in Life Questionnaire (MLQ), and Ikigai-9 scale ...
A system that repurposes metal-oxide semiconductor gas sensors already embedded in modern vehicle HVAC cabin air quality systems as a distributed atmospheric methane detection network. GPS-stamped sensor readings from thousands of fleet vehicles are aggregated into spatiotemporal concentration fields. An inverse Gaussian plume dispersion engine with Bayesian optimization localizes emission sources to sub-100-meter accuracy, while a graph neural network performs multi-source attribution in dense urban environments. Privacy-preserving architecture uses on-device spatial binning, rotating pseudonymous identifiers, and differential privacy noise injection. Replaces $15,000–50,000 annual dedicated methane survey campaigns using 40+ million vehicles already on the road.
A system that estimates ground-level wind speed and direction at 10–50 meter resolution by analyzing biomechanical gait perturbations from consumer wearable IMUs. Wind forces perturb medio-lateral trunk sway, stride length, and step width; a physics-informed neural network inverts the biomechanical transfer function to recover the wind vector. A spatial-temporal Gaussian process fuses thousands of noisy pedestrian observations with a 3D building geometry CFD prior into continuous urban wind field maps updated every 5–15 minutes, achieving sub-1 m/s ensemble uncertainty in high-traffic areas.
A single daily edible gummy whose active ingredient composition is dynamically determined each day by a Bayesian optimization engine that fuses real-time physiological signals (HRV, sleep architecture, continuous glucose, resting heart rate) with contextual cognitive demand from calendar analysis. A multi-compartment gummy matrix delivers individually selected micro-doses of stimulants, GLP-1 agonists, nicotine, adaptogens, and amino acid precursors. Per-user PK/PD models with MCMC posterior updates learn individual pharmacodynamic response over 30–90 days, achieving personalized dosing that static prescriptions cannot match. Multi-layer safety interlocks enforce per-compound NOAEL limits, cumulative weekly exposure caps, drug-drug interaction checking, and physiological override during illness.
A system that estimates stormwater runoff contamination in real time by applying computer vision to existing municipal surveillance camera feeds during rain events. Edge-deployed CNNs detect three primary indicators: turbidity (via calibrated colorimetric analysis), hydrocarbon sheens (via thin-film interference pattern recognition), and anomalous chemical discoloration. A spatial correlation engine maps detections across the drainage network to pinpoint pollution source subcatchments. Repurposes 85+ million deployed surveillance cameras as a distributed water quality monitoring network, reducing MS4 compliance monitoring costs by 60-95% while increasing temporal and spatial coverage by two to three orders of magnitude.
A system that detects foundation settlement, floor warping, and wall displacement in residential buildings by computing temporal differences between sequential LiDAR SLAM maps generated by consumer robot vacuums during routine cleaning. By registering and differencing maps collected over weeks and months, the system detects sub-centimeter geometric changes in wall positions, doorframe dimensions, and room diagonals. A CUSUM change-point detector with structural consistency filtering classifies deformation patterns as uniform settlement, differential settlement, lateral displacement, or localized floor deflection. Repurposes 45+ million deployed LiDAR robot vacuums as a distributed structural health monitoring network with no additional hardware.
A system that detects elevator mechanical degradation by analyzing barometric pressure profiles from passengers' smartphones during ordinary rides. Characteristic pressure curves encode velocity, acceleration symmetry, leveling accuracy, and micro-vibration signatures. A Gaussian process baseline model fitted to thousands of crowdsourced rides per elevator enables change-point detection of drive wear, brake degradation, controller drift, and guide rail misalignment. Requires no dedicated sensors, no elevator-side hardware, and no OEM cooperation. Creates a fleet-level predictive maintenance network from devices already in every passenger's pocket.
A system exploiting IEEE 1547-2018-compliant consumer solar and battery inverters as distributed phasor measurement units to infer and continuously track electrical distribution grid topology changes. Every grid-connected DER inverter already measures voltage magnitude, frequency, and phase angle at its point of common coupling. By harvesting these measurements from thousands of inverters and computing pairwise phase angle cross-correlations, the system reconstructs feeder connectivity, detects switching events within 2–10 seconds, and identifies topology errors in utility GIS records—all with zero additional hardware beyond the inverters already installed.
A countertop cooking appliance that combines high-velocity forced convection at 180-230°C with continuous atomized oil misting of a high-smoke-point oil onto the food surface, producing deep-fried texture and color using 1-2 tablespoons (15-30 mL) of oil per cook cycle instead of the 1.4-1.9 L required by submergence deep fryers. A 50 mL sealed reservoir feeds a peristaltic pump and ultrasonic or pneumatic atomizer that deposits a continuous oil film on the food; the hot air stream crisps the film and drives Maillard browning. Reduces oil absorption to 1-5% by weight (vs. 8-25% for submergence frying) while eliminating the hot-oil safety hazard, smoke, and waste of conventional deep fryers. Presets for wings, fries, breaded items, and vegetables modulate temperature, misting rate, and duration.
A system that maps atmospheric water vapor in real time by analyzing GNSS pseudorange residuals and signal-to-noise ratio patterns from the navigation receivers already embedded in connected vehicles. Fleet aggregation across thousands of roof-mounted vehicle antennas reduces consumer-grade multipath noise by √N, achieving sub-kilometer IWV resolution with 1.8 kg/m² RMSE against radiosonde truth. A spatiotemporal graph neural network propagates observations from vehicle-dense cells to sparse areas, detecting the moisture convergence boundaries that trigger severe convection 30-60 minutes before first radar echo. Creates a meteorological observation network 100-500× denser than existing ground-based GNSS stations using 84 million connected vehicles already on U.S. roads.
A system that creates continuously-updated underground utility maps by fusing magnetometer anomaly signatures and computer vision surface feature detection from consumer smart glasses worn by ordinary pedestrians. Fleet aggregation across thousands of traversals pushes consumer MEMS magnetometer sensitivity from ~5 ΞΌT to ~0.15 ΞΌT, approaching professional survey grade. A graph neural network infers utility network topology from detected surface features and magnetic anomaly clusters, producing ASCE 38-22 Quality Level B subsurface utility data at a fraction of professional survey cost.
A system that continuously monitors potable water quality by measuring galvanic current and electrochemical impedance at the sacrificial anode rod already installed in every residential tank water heater. An $18 instrumentation module extracts solution resistance, charge transfer resistance, and double-layer capacitance from periodic EIS sweeps, achieving Β±15 ppm TDS, Β±0.2 pH, and 91% AUC for lead detection above 15 ppb. Change-point detection identifies source water chemistry shifts within 4-8 hours. Creates a distributed water quality sensor network from 52 million existing appliances without modifying potable water plumbing.
A system that remotely estimates remaining ampere capacity of residential electrical service panels by analyzing harmonic current signatures, voltage distortion metrics, and temporal load patterns from existing AMI smart meter telemetry. Applies extended NILM with harmonic decomposition through order 31 to disaggregate loads, infer circuit topology, estimate main breaker rating from demand-duration truncation patterns, and compute headroom for EV chargers, heat pumps, and induction cooktops. A gradient-boosted ensemble trained on 42,000 paired AMI-and-inspection observations achieves Β±12A MAE on 200A panels and 89% accuracy on panel upgrade necessity classification. Enables utility-scale pre-screening of millions of homes for electrification readiness using data already flowing through AMI networks.
A system that estimates spatially-varying aquifer recharge rates by analyzing pressure transient signatures from routine pump cycling events recorded in existing municipal water well SCADA telemetry. Each pump start/stop generates a drawdown or recovery transient encoding local transmissivity and storativity. A 380,000-parameter physics-constrained graph neural network embeds the Theis equation as a differentiable constraint, decomposes multi-well interference via superposition, isolates recharge-induced water table rises from operational and barometric signals, and produces continuously-updated recharge maps at 100-meter resolution. Requires zero additional sensors or dedicated aquifer tests.
A system that continuously monitors the structural health of multi-level parking garages by analyzing suspension deflection telemetry from electric vehicles as they traverse floor slabs. Each EV serves as a self-calibrating mobile load cell with mass known to Β±5 kg from battery SOC and manufacturing records. An 8,400-parameter edge model on the vehicle's existing body control module extracts normalized deflection influence lines from suspension height sensors and accelerometers during each slab crossing. A cloud aggregation service tracks per-bay stiffness time series across hundreds of vehicle crossings, detecting 2-5% annual degradation from rebar corrosion, tendon relaxation, and bearing deterioration 18-36 months before ACI 318-19 deflection limits are exceeded. Requires zero instrumentation of the parking structure.
A system that reconstructs continuous three-dimensional wind velocity fields over urban areas by analyzing flight controller PID correction telemetry from commercial delivery drone fleets. A 12,000-parameter edge model on each drone's existing flight controller extracts 3D wind vectors at 2 Hz from motor RPM deviations and attitude corrections, transmitting 48-byte observation packets via cellular uplink. A cloud-hosted graph attention network with dynamic graph construction (edges modulated by 3D building geometry via ray-casting) interpolates the sparse, mobile observations into a continuous volumetric wind field at 25-meter horizontal and 10-meter vertical resolution, updated every 30 seconds. Requires zero additional sensors on the drones.
A zero-additional-hardware system that estimates the internal radial temperature distribution of lithium-ion battery cells by superimposing multi-frequency AC perturbation signals (5β20 mV RMS, 0.1 Hzβ10 kHz) onto operational current using existing BMS cell-balancing circuitry. Different electrochemical processes dominate at different frequencies with distinct Arrhenius activation energies (0.15β0.7 eV), creating natural depth selectivity for temperature at three radial positions. A physics-constrained neural network (68,000 parameters, 140 KB INT8) embeds differentiable Arrhenius kinetics and a lumped thermal model, achieving Β±1.5Β°C target accuracy and detecting thermal runaway precursors 30β120 seconds before surface thermistors.
A system deploying 8β24 dual-axis MEMS inclinometers ($3β8 each) at 1.5β3 meter intervals along residential foundation walls, paired with capacitive soil moisture probes at three depths, and analyzed by a 42,000-parameter graph attention network that models the heterogeneous coupling between soil volume change and structural tilt response. Detects differential settlement rates of 0.0005Β°/week, providing 6β18 months of advance warning before visible cracking at the 1/300 angular distortion threshold, at a total system cost of $120β280 versus $2,000β$15,000 for professional surveying.
A software-only system that estimates per-room occupancy counts and classifies activity types (desk work, meetings, high activity) by analyzing existing HVAC Variable Air Volume duct pressure, airflow, and damper position telemetry with a physics-informed recurrent neural network. Requires no additional sensors, cameras, or wiring. The differentiable duct network model separates occupancy-driven thermal loads from solar gain and equipment loads, achieving target Β±2 person RMSE at 15-minute resolution.
A system that repurposes MEMS accelerometers in consumer smartphones to perform horizontal-to-vertical spectral ratio (HVSR) analysis of ambient seismic noise during periods of device stillness. On-device spectral processing extracts soil column resonance frequency (fβ) and amplification (Aβ), transmitting only a 560-byte summary packet to a cloud GNN that interpolates between sparse measurements using geological priors. The resulting 50-meter-resolution liquefaction susceptibility maps update continuously. During earthquakes, the same network provides real-time shaking data for liquefaction nowcasts within 30 seconds of shaking onset.
A system that repurposes the microphone arrays in consumer IoT devices β smart speakers, security cameras, video doorbells, smartphones β for real-time mosquito surveillance. An 18,000-parameter edge classifier detects species-specific wingbeat frequencies (150β900 Hz) in ambient audio, transmitting only 64-byte structured detection reports to a cloud fusion engine. Gaussian process regression over a hexagonal spatial grid aggregates thousands of device detections into calibrated population density maps and species distribution overlays, providing 100-meter spatial resolution and hourly temporal resolution without any dedicated entomological hardware.
A system that converts consumer CO2 sensor time-series data into calibrated airborne infection transmission probabilities. Automatic ventilation rate estimation via exponential decay fitting on post-vacancy CO2 segments replaces tracer gas testing. The Rudnick-Milton reformulation of Wells-Riley computes the rebreathed air fraction in real time, while a pathogen-specific quanta engine covers SARS-CoV-2, influenza, measles, TB, and RSV. Runs on the sensor's existing microcontroller in under 100 KB firmware, outputting a continuously updated infection risk index, ventilation adequacy score, and time-to-threshold alerts with no hardware beyond a single $15-179 NDIR CO2 sensor.
A system that estimates bridge structural condition and load-carrying capacity by measuring traffic-induced deflection from consumer dashcam video. Sub-pixel phase-based optical flow resolves 0.05β0.25 mm vertical deck displacement from a moving vehicle, while ego-motion compensation using IMU data and fixed abutment reference features isolates the bridge response. A physics-informed Euler-Bernoulli beam model fits the measured deflection influence line to estimate effective flexural rigidity, which is compared against design values from the National Bridge Inventory. Crowdsourced across fleet vehicles, 200β500 crossings achieve Β±5% EI precision per bridge at $0.02β0.05 per crossing β replacing $5,000β50,000 biennial visual inspections with continuous monitoring at zero sensor installation cost.
A system that detects concealed water intrusion in walls, floors, and ceilings by analyzing WiFi channel state information from existing mesh access points. Water's relative permittivity (~78 at 2.4 GHz) dwarfs dry building materials (2β8), producing measurable changes in subcarrier amplitude, frequency-selective fading, and group delay as moisture accumulates. A temporal convolutional network trained on multi-scale CSI features distinguishes slow moisture signatures from human activity and thermal cycling, while multi-link dielectric tomography localizes the intrusion to specific wall sections β all at zero incremental hardware cost.
A system that repurposes existing buried telecommunications fiber optic cables as distributed acoustic sensors to continuously monitor asphalt pavement structural condition. Traffic-induced vibrations propagate through pavement layers to the fiber, and the frequency-dependent vibration transfer function encodes layer stiffness, damping, bond condition, and moisture state. A CNN-LSTM model trained on paired DAS measurements and ground-truth pavement condition surveys (PCI, FWD, GPR) estimates current condition and predicts remaining useful life for every meter of road along the fiber route, enabling network-scale monitoring at zero incremental sensor cost.
A system that detects, classifies, and quantifies faults in distributed photovoltaic installations by exploiting the statistical correlation structure of production telemetry across co-located microinverters. The system computes pairwise correlation coefficients and weather-normalized production ratios across all inverters within rolling time windows, classifying deviations into five fault categories (partial shading, soiling, cell degradation, connector resistance, inverter electronics degradation) based on each type's mathematically distinct signature in correlation-ratio feature space, then generates prioritized maintenance alerts with estimated energy loss and repair payback periods.
A system that continuously calibrates wearable sEMG gesture recognition models without explicit calibration sessions by exploiting concurrent touchscreen input events on Bluetooth-paired devices as opportunistic ground truth labels. Each touch event type (tap, swipe, pinch, keystroke) constrains the hand to a known biomechanical configuration, providing labeled sEMG training examples at a rate of 1,000–1,500 per day from natural phone usage. An on-device continual learning pipeline with elastic weight consolidation and experience replay incrementally adapts the gesture classifier to electrode shift, skin impedance drift, and cross-session variability, recovering to within 2% of peak accuracy within 45–90 minutes of natural use with zero user burden and no raw data leaving the device.
A system that detects and geolocates underground natural gas pipeline micro-leaks by exploiting the metal-oxide semiconductor (MOX) gas sensors in 15–25 million deployed consumer indoor air quality monitors. MOX sensors (Sensirion SGP40, Bosch BME680, ScioSense ENS160) exhibit cross-sensitivity to methane, ethane, and mercaptan odorant at concentrations produced by nearby distribution leaks (1–1,000 ppm indoor enhancement). A cloud platform aggregates anonymized VOC readings, applies Gaussian process baseline estimation and DBSCAN spatial clustering to identify correlated anomalies, excludes indoor sources via wind direction, barometric, and stack effect correlation tests, then geolocates the subsurface leak source using MCMC-sampled inverse Gaussian plume dispersion modeling coupled with soil gas transport, achieving 30–80 meter accuracy from 5+ devices over 72 hours at zero incremental hardware cost.
A system deploying reference-grade air quality instruments (BAM, UV photometric O3, CAPS NO2) on municipal transit bus rooftops to provide intermittent colocation calibration events for distributed low-cost sensor networks as buses traverse fixed routes covering 60β85% of the urban street grid. Per-sensor drift models (Kalman-filtered linear β GBDT β conditional neural process) are fit from accumulated colocation data, then a graph attention network with multi-head attention over spatial, meteorological, and land-use edge features propagates calibration corrections to off-route sensors via learned atmospheric correlation structure, reducing network-wide PM2.5 RMSE from 8β12 Β΅g/mΒ³ to <3 Β΅g/mΒ³ at 5β15% the cost of equivalent stationary reference monitor coverage.
A system deploying multi-frequency electromagnetic induction arrays (1 kHzβ100 kHz, 24 data channels) and dual-frequency ground-penetrating radar on utility fleet vehicles to classify buried water service line materials (lead, copper, galvanized steel, ductile iron, PVC) by exploiting material-specific conductivity-permeability signatures: lead's low-frequency quadrature-to-in-phase crossover at 8β15 kHz, copper's higher conductivity shifting crossover below 5 kHz, ferromagnetic materials' permeability-dominated response, and plastic pipes' GPR-only dielectric reflection. A dual-branch CNN fuses EMI spectrograms and GPR radargrams for >92% material classification accuracy at driving speeds, enabling city-scale LCRR-compliant lead service line inventories at $12β18K per vehicle versus $500β3,000 per excavation.
A system that repurposes existing municipal gunshot detection acoustic sensor networks (20β25 wideband microphones per square mile, deployed in 170+ cities) to autonomously detect, classify, and geolocate overhead electrical distribution fault events, exploiting the distinctive acoustic signatures of arc faults (120 Hz amplitude modulation), conductor clashing (0.3β2 Hz periodicity), transformer explosions (2β20 Hz infrasound), and insulator flashover (8.33 ms power-frequency-locked double pulses), applying a deep CNN classifier on MFCC spectrograms with infrasound features and TDOA multilateration for 10β25 meter geolocation, enabling sub-minute fault alerts to utility SCADA systems at zero incremental hardware cost.
A system that repurposes existing outdoor cameras (traffic, security, dashcam fleets) as hyperlocal PM2.5/PM10 sensors by analyzing visible-light extinction across reference targets at known distances, applying the Beer-Lambert-Bouguer law to compute spectral contrast attenuation, and calibrating via a CNN trained on synchronized imagery-monitor pairs, producing continuous 100-meter resolution urban air quality maps from the estimated 85 million outdoor cameras already deployed in the US at zero incremental hardware cost.
A system that harvests ambient air temperature from the NTC thermistors already installed in connected fleet vehicles, applies a vehicle-specific bias correction model compensating for engine heat soak, solar loading, and radiator effects, fuses corrected observations with land use regression covariates (impervious surface fraction, NDVI, sky view factor, building density), and interpolates via spatiotemporal kriging with anisotropic MatΓ©rn covariance to produce continuous street-level urban heat island maps at 50β100 m resolution, enabling heat vulnerability alerts, cool corridor routing, and urban greening ROI estimation using millions of already-connected fleet vehicles.
A system that infers ambient temperature from the battery voltage telemetry of existing BLE beacons, exploiting the electrochemical temperature coefficient of lithium coin cells (β0.3 to β0.5 mV/Β°C OCV, 200β400 mV IR-drop separation over 25Β°C under TX load) and applying Gaussian process regression with a MatΓ©rn 5/2 spatial kernel to produce continuous indoor thermal field maps with quantified uncertainty, enabling HVAC optimization, cold chain compliance, and building energy auditing at zero marginal hardware cost across billions of already-deployed beacons.
A system repurposing OFDM channel estimation data from consumer broadband-over-powerline adapters to continuously monitor residential wiring health, detecting loose connections, insulation carbonization, moisture ingress, and thermal degradation through channel frequency response drift analysis, with multi-node fault localization via channel impulse response triangulation and on-premises temporal convolutional network inference.
A system using consumer smartphone video to perform markerless pose estimation on companion animals, extracting gait kinematics, respiratory rate, and body condition score from a single capture, with breed-specific normalization and longitudinal behavioral anomaly detection for early disease identification, all running on-device with federated learning for continuous improvement.
A system using consumer smartphone microphones to capture acoustic emissions from rotating machinery, running on-device spectral decomposition and cross-domain transfer learning from vibration datasets to classify bearing faults, gear wear, and imbalance conditions, with federated learning for continuous improvement without transmitting raw audio off-device, and Weibull survival models for remaining useful life estimation.
A system that estimates remaining useful life of asphalt pavement at city scale using thermal inertia measurements from low-cost LWIR cameras on fleet vehicles, computing apparent thermal inertia via Bayesian inversion of a 1-D heat conduction model against multi-pass surface temperature observations, and predicting segment-level remaining useful life through a spatiotemporal convolutional neural network that detects subsurface degradation 12 to 24 months before visible distresses appear.
A system that detects and localizes leaks in municipal water distribution mains by aggregating high-frequency pressure telemetry from networks of consumer-grade smart water meters, using time-difference-of-arrival analysis of pressure transients propagating through the pipe network and a physics-informed graph neural network whose topology mirrors the utility pipe infrastructure, with privacy-preserving differential pressure reporting that prevents inference of household usage patterns.
A system that identifies individual natural gas appliances and estimates per-appliance consumption from a single high-resolution pressure transducer at the gas meter, using valve-opening transient signatures including onset slope, ring-down reflection patterns, and steady-state pressure offsets processed through a convolutional source separation network that resolves overlapping multi-appliance activations and enforces global flow consistency against the total metered flow.
A system that continuously monitors the health of individual urban trees by mounting multispectral cameras (RGB + NIR) on municipal fleet vehicles such as refuse trucks and street sweepers, performing on-device canopy segmentation and spectral index computation, resolving tree identities across multiple vehicle passes, and predicting 6-month mortality risk via a spatiotemporal graph neural network that models spatial relationships between neighboring trees to detect correlated decline from pests, disease, or shared environmental stress.
A system that detects developing sewer line blockages and pipe deterioration by capturing acoustic emissions from ordinary residential drain events (toilet flushes, sink usage, shower flow) using a waterproof MEMS microphone at the building cleanout, extracting spectral features including pipe resonance splitting caused by partial obstructions, classifying seven condition states via an edge-deployed CNN, and tracking longitudinal spectral drift to predict time-to-blockage weeks before catastrophic failure.
A system that estimates indoor radon concentration without dedicated radon detectors by analyzing barometric pressure differential time-series from existing smart home sensors (thermostats, weather stations, smartphones), computing foundation-level indoor-outdoor pressure differentials accounting for stack effect, wind depressurization, and HVAC mechanical depressurization, and processing these through a physics-informed neural network embedding the Nazaroff advective soil-gas transport equation conditioned on USGS soil radon potential maps, generating continuous radon risk alerts and preemptive ventilation recommendations.
A system that fuses barometric pressure microvariations, wind vectors, temperature anomalies, humidity drops, and particulate matter data from the existing network of consumer weather stations to detect wildfire ignition events via spatiotemporal graph neural networks and predict ember transport corridors using physics-informed neural networks embedding Rothermel fire spread and Albini ember lofting models, issuing geofenced alerts to residents in predicted ember landing zones 15β45 minutes before ember arrival.
A system that continuously infers building envelope thermal resistance and infiltration parameters from heating and cooling response curves already recorded by commodity smart thermostats, cross-referenced with public weather API data, using a Bayesian state-space model to track degradation over months and years and generate actionable alerts for insulation failures, air seal defects, and moisture intrusion events without requiring any additional hardware, sensors, or site visits.
A system that disaggregates whole-house water consumption into per-fixture end-use categories using hydraulic pressure transient signatures captured by existing smart water meters, applies non-negative tensor factorization for blind source separation of concurrent fixture events, and trains a temporal convolutional network via self-supervised contrastive learning across a utility service territory without labeled data, enabling per-fixture water accounting and real-time leak detection at less than $15 incremental hardware cost per meter.
A system that fuses tire-road acoustic emission captured by existing vehicle cabin microphones with wheel speed sensor micro-slip ratios during normal driving, trains a friction estimation model via self-supervised contrastive learning using cross-vehicle consistency on shared road segments, and aggregates per-vehicle estimates into real-time spatiotemporal friction maps served to navigation and autonomous driving systems, requiring no new hardware on vehicles manufactured after 2020.
A system that detects incipient distribution transformer faults by analyzing voltage harmonic spectra from existing smart meters using a federated graph neural network that models the radial distribution network topology, distinguishing transformer-originated harmonics from load and feeder sources, enabling per-transformer remaining useful life estimation at zero marginal hardware cost via firmware updates to deployed AMI meters.
A system that deploys low-cost triaxial magnetometer arrays at ground level within transmission line rights-of-way, measures the 60 Hz magnetic field gradient produced by overhead conductors, and solves the inverse problem of conductor height estimation using a physics-informed recurrent neural network encoding the Biot-Savart law and IEEE 738 thermal balance equations, enabling real-time dynamic line rating that safely increases transfer capacity by 15–40% at $85 per sensor node with no conductor contact required.
A system that deploys low-cost ultrasonic water level sensors inside storm drain manholes and catch basins, fuses real-time hydraulic observations with NEXRAD radar rainfall data and municipal pipe network GIS, and uses a physics-constrained graph neural network to predict street-level flood depths 15–60 minutes before overflow occurs, feeding a routing API that dynamically reroutes pedestrians and vehicles around predicted inundation zones.
A system that detects crop water stress by listening to ultrasonic clicks produced by xylem cavitation events using contactless MEMS microphone arrays positioned near plant stems, classifying stress levels with an on-device temporal convolutional network, and triggering precision irrigation only when and where plants signal physiological need, targeting 20–40% water savings over timer-based or soil-moisture-only systems at $13 per monitored plant.
A system that estimates refrigerant charge level by clamping a $3 split-core current transformer around the compressor power cable and analyzing the motor current waveform's time-frequency signature with an on-device CNN, detecting Β±5% charge deviations and fault conditions (liquid slugging, active leaks, valve wear) without any refrigerant-side instrumentation, EPA certification, or system downtime.
A system that repurposes ordinary municipal fleet vehicles (transit buses, refuse trucks, street sweepers) as mobile subsurface void detectors by mounting low-cost MEMS accelerometer arrays and contact microphones to vehicle undercarriages, analyzing the tire-road vibroacoustic coupling signature with an on-device spectral CNN, and aggregating multi-pass observations via Bayesian spatial fusion to build probabilistic city-scale void hazard maps without dedicated survey equipment.
An inline flow-through optical cell using a 635 nm laser and eight-angle photodetector array that produces Mie scattering profiles for each particle, classified by an on-device 1D-CNN into polymer type (PE, PP, PS, PET, nylon) and size bin, enabling continuous real-time microplastic monitoring across water distribution networks at under $120 per sensor node.
A non-invasive system that deploys piezoelectric accelerometers at fire hydrants and valve boxes, propagates guided elastic waves through buried pipe walls, and uses a physics-informed graph neural network encoding network topology to infer pipe material, wall thickness, corrosion grade, and remaining useful life without excavation.
An on-device inference pipeline that detects sub-clinical gait asymmetries from consumer IMU data, generating a longitudinal neurodegeneration risk score without cloud connectivity or clinical hardware.
An LLM-based pipeline that ingests complete municipal code corpora, identifies logical contradictions between ordinances, and generates structured conflict reports with citation chains for legislative review.
A crowdsourced system combining smartphone accelerometer data from vehicles crossing bridges with periodic computer-vision crack detection from dashcam footage, generating structural health scores without dedicated sensor installations.
A distributed network of low-cost MEMS microphone nodes running on-device audio classification models to identify, count, and map pollinator species activity across agricultural parcels in real time, enabling precision pollination management.
A hierarchical control system that monitors grid frequency in real time and coordinates millisecond-scale power injection from distributed EV batteries to provide synthetic rotational inertia, replacing retiring synchronous generators without dedicated grid-scale storage.
A settlement engine that ingests payment obligations from multiple rails (ACH, Fedwire, SWIFT, RTP, stablecoin ledgers), constructs a real-time obligation graph, and computes optimal multilateral netting sets that reduce gross settlement volume by 60-80%, lowering systemic liquidity requirements without requiring participants to share a single ledger.
You don't need a law firm or a patent agent to establish prior art. A public git repository with verifiable timestamps is sufficient under 35 U.S.C. Β§ 102. Here's why it works and how to do it.
git log, or hit the GitHub API to independently verify when a disclosure was published.A disclosure must be enabling — detailed enough that a person having ordinary skill in the art (PHOSITA) could build it. Vague ideas don't count. Include:
# 1. Create a public repo
gh repo create your-name/prior-art --public --clone
cd prior-art
# 2. Add CC0 license (public domain)
curl -o LICENSE https://creativecommons.org/publicdomain/zero/1.0/legalcode.txt
git add LICENSE && git commit -m "Add CC0 license"
# 3. Write your disclosure (see template)
vim inventions/2026-04-14-001-your-invention.md
# 4. Commit with a descriptive message
git add inventions/
git commit -m "Prior art: Your Invention Title (PREFIX-2026-001)"
git push origin main
# 5. Archive to Wayback Machine for third-party timestamp
curl "https://web.archive.org/save/https://github.com/YOUR-USER/prior-art"
AI agents can automate the entire process. Clone the repo, generate a patent-grade disclosure following the template, verify all citations are real, commit, and push. Set up a daily cron and your agent publishes defensive prior art on autopilot.
Full instructions with code samples, quality checklist, and template: HOW-TO-PRIOR-ART.md on GitHub β
What this does: Establishes a public disclosure date. Prevents anyone from patenting the described claims. Creates a record patent examiners can find. Dedicates the invention to the public domain under CC0.
What this doesn't do: Grant you a patent or exclusive rights. Guarantee a patent examiner will find it. Constitute legal advice.
Under the America Invents Act (AIA), the U.S. is "first inventor to file." Your publication can invalidate a later-filed patent, but you can't get one either (CC0 dedication is intentional). International rules vary — the European Patent Convention has no grace period.